Review

Managing chronic kidney disease according to KDIGO risk categories: A primer for primary care

Rodolfo J. Galindo, MD, Diana Soliman, MD, Joshua J. Neumiller, PharmD, Jay H. Shubrook, DO and Katherine R. Tuttle, MD
Cleveland Clinic Journal of Medicine June 2026, 93 (6) 353-365; DOI: https://doi.org/10.3949/ccjm.93a.25072

ABSTRACT

The increasing prevalence of type 2 diabetes is contributing substantially to chronic kidney and cardiovascular disease. The updated Kidney Disease: Improving Global Outcomes (KDIGO) heat map provides a practical framework for stratifying patient risk, using estimated glomerular filtration rate (eGFR) and albuminuria to facilitate early detection and evidence-based management. However, real-world adoption of the heat map remains limited. Integrating this tool into primary care and other subspecialities beyond nephrology can enhance individualized care. This review highlights the KDIGO guideline updates and provides an approach to implementing the heat map in primary care.

KEY POINTS

  • Screen at-risk patients for chronic kidney disease using both eGFR and urinary albumin-to-creatinine ratio; diagnosis is confirmed if abnormal results persist for over 3 months.

  • Use the KDIGO heat map to classify chronic kidney disease risk and guide how often to monitor kidney function and when to start medical therapies.

  • Primary care clinicians can start guideline-directed treatment without waiting for specialist referral.

  • Display the KDIGO heat map in clinical areas and use it to support shared decision-making and timely nephrology referral for high-risk patients.

The prevalence of type 2 diabetes is rising globally.1 About 40% of patients with type 2 diabetes develop diabetic kidney disease, a major contributor to cardiovascular disease and chronic kidney disease.2 The shared mechanisms and risk factors among diabetes, chronic kidney disease, and cardiovascular disease have led to the concept of cardiovascular-kidney-metabolic syndrome,3 now formally defined by the American Heart Association as the interplay of obesity, diabetes, chronic kidney disease, and various cardiovascular conditions.4,5 Cardiovascular-kidney-metabolic syndrome presents both a public health challenge and an opportunity for intervention, as new therapies can reduce kidney failure, cardiovascular disease events, and mortality,6,7 but awareness and screening remain low among patients and healthcare professionals.8,9

See related editorial, page 366

This review examines the updated 2024 Kidney Disease: Improving Global Outcomes10 (KDIGO) heat map and guideline for managing chronic kidney disease, focusing on practical education and application for healthcare teams, and provides a simplified treatment algorithm and analysis of outcomes by KDIGO risk category. While this review focuses on KDIGO recommendations for adults, it is important to mention that the guideline provides chronic kidney disease information and recommendations across the lifespan.

THE HEAT MAP CONCEPT

The 2012 KDIGO guideline11 defined chronic kidney disease as abnormalities in kidney structure or function lasting at least 3 months. The CGA classification system was used, which incorporated the underlying cause of chronic kidney disease, glomerular filtration rate (GFR) category (G1–G5), and albuminuria category (A1–A3). Management recommendations were based on stratifying patients by both estimated GFR (eGFR) and urinary albumin-to-creatinine ratio, using a heat map to categorize risk for cardiovascular disease, kidney failure, and all-cause mortality as low (green), moderate (yellow), high (orange), or very high (red).

2024 KDIGO UPDATES REVISE CLASSIFICATION SYSTEM

The updated 2024 KDIGO guideline10 builds on the foundation from the 2012 guideline11 by revising the chronic kidney disease classification system within the CGA framework. A key change is the recommendation to use both creatinine and cystatin C for more accurate GFR estimation across clinical settings. The 2024 guideline also promotes risk prediction models that incorporate chronic kidney disease–related variables to better forecast disease progression and complications. This personalized approach reflects precision-medicine principles by considering factors such as age, sex, and comorbidities in treatment planning.

The updated heat map (Figure 1) refines chronic kidney disease risk classifications based on combined eGFR and urinary albumin-to-creatinine ratio to support diagnosis and management. The risk stratification is based on data from the Chronic Kidney Disease Prognosis Consortium’s meta-analysis, which included over 27 million participants across 114 global cohorts.12 This comprehensive analysis established clear dose-response relationships between reduced eGFR, increased albuminuria, and higher risks of kidney failure requiring replacement therapy, cardiovascular disease, and all-cause mortality. These graded associations directly informed the color-coded risk stratification system—green indicates the lowest risk and red the highest—to predict adverse outcomes.

Figure 1
Figure 1

Kidney Disease: Improving Global Outcomes heat map for classifying chronic kidney disease (CKD). The numbers in the boxes indicate the number of times annually a patient should be screened. Asterisk indicates very high risk; treatment and monitoring frequency should be based on disease progression and the patient’s clinical status.

GFR = glomerular filtration rate

Reprinted from de Boer IH, Khunti K, Sadusky T, et al. Diabetes management in chronic kidney disease: a consensus report by the American Diabetes Association (ADA) and Kidney Disease: Improving Global Outcomes (KDIGO). Diabetes Care 2022; 45(12):3075–3090. doi:10.2337/dci22-0027. Copyright and all rights reserved. Material from this publication has been used with the permission of the American Diabetes Association.

IMPORTANCE OF KDIGO APPLICATION IN PRIMARY CARE FOR EARLY INTERVENTION

The 2024 KDIGO guideline10 emphasizes multidisciplinary chronic kidney disease detection across primary care, cardiology, and endocrinology, prioritizing early screening via eGFR and urinary albumin-to-creatinine ratio in at-risk patient populations (detailed below). Risk stratification enables tailored interventions: higher-risk patients receive intensive treatment to address both kidney function decline and cardiovascular risks inherent to cardiovascular-kidney-metabolic syndrome.11

Primary care clinicians play a critical role in early initiation of guideline-directed medical therapy. Delays in starting evidence-based treatments like renin-angiotensin system inhibitors, sodium-glucose cotransporter (SGLT) 2 inhibitors, nonsteroidal mineralocorticoid antagonists, or glucagon-like peptide (GLP) 1 receptor agonists heighten risks of irreversible complications. Treatment should begin at diagnosis and be guided by ongoing risk assessment rather than specialist referral, though specialist referral is warranted in many cases.11

A structured risk-stratification framework simplifies decision-making, addressing chronic kidney disease progression and cardiovascular-kidney-metabolic complexities. Collaborative management ensures continuity between primary care (initiating medical therapies) and specialists (managing advanced cases). Figure 2 presents a simplified treatment algorithm for non-nephrologists, focusing on medication sequencing and monitoring.

Figure 2
Figure 2

Kidney Disease: Improving Global Outcomes (KDIGO) application steps in the primary care setting.

aDespite metformin or sodium-glucose cotransporter (SGLT) 2 inhibitor treatment or are unable to use those medications.

bNot treated with chronic dialysis or kidney transplantation.

cDihydropyridine calcium channel blocker, diuretic, or both if needed to achieve individualized blood pressure target.

ASCVD = atherosclerotic cardiovascular disease; CAD = coronary artery disease; CVD = cardiovascular disease; eGFR = estimated glomerular filtration rate; eGFRcr = estimated glomerular filtration rate based on serum creatinine; eGFRcr-cys = estimated glomerular filtration rate based on serum creatinine and cystatin C; GLP-1 = glucagon-like peptide 1; nsMRA = nonsteroidal mineralocorticoid receptor; PCSK9i = proprotein convertase subtilisin/kexin type 9 inhibitor; RASi = renin-angiotensin system inhibitor; sMRA = steroidal mineralocorticoid receptor

Based on information from reference 10.

KDIGO APPLICATION STEPS IN CLINICAL PRACTICE

Step 1: Identify at-risk patients

At-risk patients have any of the following established risk factors10:

  • Hypertension

  • Diabetes

  • Cardiovascular disease

  • Family history of chronic kidney disease

  • History of acute kidney injury.

Other patients with elevated risk are those with systemic lupus erythematosus, human immunodeficiency virus infection, obesity, genetic predispositions, and exposure to nephrotoxic agents. Early targeted screening for chronic kidney disease is key, as it allows for detection at earlier stages before progression to more advanced and difficult-to-manage stages.

Step 2: Test kidney function

This step involves measuring serum creatinine and cystatin C, where available, to assess kidney function. Kidney injury is evaluated using the urinary albumin-to-creatinine ratio or, in its absence, a urine dipstick test. Calculating the eGFR using both creatinine and cystatin C enhances accuracy and is preferred if available. In addition, obtaining eGFR based on creatinine and cystatin C or cystatin C alone offers a more precise evaluation of kidney function and improves overall risk stratification in cases where creatinine levels may be uncertain, such as in patients with extremely low or high body weight. Point-of-care blood or saliva creatinine and urine albumin tests can be used where available; when quality control measures are taken, these tests provide sufficient accuracy to support the clinical pathway in settings with limited laboratory resources.13–15

Clinicians should be aware that both eGFR and urinary albumin-to-creatinine ratio levels can exhibit random fluctuations that may not be clinically relevant. However, an increase or decrease in eGFR of more than 30% likely indicates an abnormal alteration in kidney function.10 Similarly, an increase or decrease in urine albumin levels of more than 30% typically surpasses random variability and suggests a meaningful change in kidney health. Also, the age-appropriate eGFR equation must be used, as equations differ for adults and children.

Step 3: Diagnose chronic kidney disease

Chronic kidney disease is diagnosed when the eGFR is less than 60 mL/min/1.73 m2, the urinary albumin-to-creatinine ratio is 30 mg/g or higher, or both, on at least 2 measurements taken more than 90 days apart. This is crucial because determining chronicity distinguishes acute kidney injury from chronic kidney disease. In older adults, chronic kidney disease should be diagnosed if the eGFR is below 60 mL/min/1.73 m2, even in the absence of significant albuminuria (urinary albumin-to-creatinine ratio < 30 mg/g).

Step 4: Determine KDIGO risk category

The next step after diagnosis is stratifying the patient with the KDIGO heat map into low, moderate, high, or very high risk of chronic kidney disease progression and its potential complications. This stratification is essential as it guides clinical decision-making and tailors treatment strategies that mitigate the risks of chronic kidney disease and associated comorbidities such as cardiovascular disease, diabetes, and hypertension.

Note that the 2024 KDIGO guideline10 recommends also using an externally validated risk equation (eg, Kidney Failure Risk Equation, Kaiser Permanente Northwest, Z6 score) for patients with chronic kidney disease stages G3 to G5 to estimate the absolute risk of kidney failure within 2 to 5 years.

Step 5: Begin treatment

Chronic kidney disease management is based on a patient’s KDIGO heat-map risk category, as it informs both monitoring frequency and therapeutic adjustments. A comprehensive treatment strategy combines lifestyle interventions—patient education, exercising 150 minutes or more per week, smoking cessation, and eating a healthy diet—with pharmacotherapy based on an individual’s risk profile.

Outlined below are the focused pharmacotherapy recommendations from the 2024 KDIGO guideline for managing chronic kidney disease.

Renin-angiotensin system inhibitors, including angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, are recommended (ie, most patients should receive them) for patients with chronic kidney disease and severely increased albuminuria (G1–G4, A3) regardless of diabetes status, and for patients with diabetes and moderately to severely increased albuminuria (G1–G4, A2 or A3). They are suggested (ie, appropriate for many, but not all, patients) for patients without diabetes and moderately increased albuminuria (G1–G4, A2). Combination therapy with angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, or direct renin inhibitors is discouraged due to risks of hyperkalemia and acute kidney injury.

SGLT-2 inhibitors are recommended for patients with type 2 diabetes, chronic kidney disease, and eGFR 20 mL/min/1.73 m2 or greater, and those with chronic kidney disease and either severely increased albuminuria (A3) or heart failure regardless of albuminuria. They are also suggested for patients with eGFR 20 to 45 mL/min/1.73 m2 and lower albuminuria (A1).

Long-acting GLP-1 receptor agonists are recommended for patients with type 2 diabetes and chronic kidney disease who have not achieved glycemic targets with metformin or SGLT-2 inhibitors, or who cannot use those medications.

Nonsteroidal mineralocorticoid antagonists with demonstrated kidney or cardiovascular benefits are recommended for adults with type 2 diabetes, eGFR 25 mL/min/1.73 m2 or greater, and albuminuria greater than 30 mg/g despite maximized renin-angiotensin system inhibitor therapy. They are particularly advised for those at high risk of chronic kidney disease progression or cardiovascular events, especially with persistent albuminuria. Nonsteroidal mineralocorticoid antagonists should be added to renin-angiotensin system inhibitors and, when appropriate, SGLT-2 inhibitors (discussed in more detail below).

Statins are recommended for adults 50 years or older with chronic kidney disease and an eGFR less than 60 mL/min/1.73 m2, as well as for younger patients (18–49 years) with chronic kidney disease who have known cardiovascular disease, diabetes, a history of ischemic stroke, or a high cardiovascular risk.

Antiplatelet therapy with low-dose aspirin, or an alternative agent in cases of intolerance, is recommended for secondary prevention of cardiovascular disease in patients with chronic kidney disease and established ischemic heart disease (KDIGO gives this a 1C rating, indicating the treatment is recommended but the certainty of evidence is low).

Proprotein convertase subtilisin/kexin type 9 inhibitors should be considered as additional risk-based medical therapy if indicated based on a patient’s atherosclerotic cardiovascular disease risk and lipid profile.

Monitor for adverse events

Starting renin-angiotensin system inhibitors, SGLT-2 inhibitors, or nonsteroidal mineralocorticoid antagonists may cause an initial eGFR dip, but a decline greater than 30% from baseline calls for reevaluation. Nonsteroidal mineralocorticoid antagonists and renin-angiotensin system inhibitors can also cause hyperkalemia, which may warrant increased monitoring and potassium-reducing interventions such as diet modifications and potassium binders rather than discontinuation. Notably, SGLT-2 inhibitors reduce the risk of hyperkalemia.16

GUIDELINE-DIRECTED MEDICAL THERAPY RECOMMENDATIONS FOR CHRONIC KIDNEY DISEASE

While the KDIGO guideline10 offers evidence-based recommendations for managing chronic kidney disease, many patients also have diabetes, making integration with the American Diabetes Association (ADA) guideline essential. The ADA guideline17,18 focuses on glycemic control and cardiovascular risk reduction, both of which are important to improve patient outcomes. Of note, the KDIGO and ADA consensus report2 aligns on the role of medical therapies like SGLT-2 inhibitors and GLP-1 receptor agonists, which benefit both kidney and cardiovascular health.

Table 12,10,17,18 provides a comprehensive overview of guideline-directed medical therapy for managing chronic kidney disease, particularly in patients with comorbid conditions like type 2 diabetes. Table 210 summarizes the potential mortality, cardiovascular, and kidney benefits of the recommended pharmacotherapy options.

View this table:
TABLE 1

Current Kidney Disease: Improving Global Outcomes (KDIGO) and American Diabetes Association (ADA) guideline-recommended medication criteria for patients with chronic kidney disease (CKD)

View this table:
TABLE 2

Benefits of pharmacotherapy recommended by Kidney Disease: Improving Global Outcomes guideline for patients with chronic kidney disease

KEY FINDINGS ACROSS KDIGO RISK CATEGORIES FROM RECENT TRIALS

The KDIGO guideline10 provides a structured framework for chronic kidney disease risk stratification and forms the basis for implementing guideline-directed medical therapy in clinical practice. By integrating these therapies within the KDIGO framework, clinicians can optimize treatment strategies based on each patient’s risk profile. Recent research, summarized below and in Table 3,7,19–29 has demonstrated the efficacy of these newer therapeutic agents across various chronic kidney disease risk categories, as well as their potential to improve renal and cardiovascular outcomes.6

View this table:
TABLE 3

Summary of recent clinical trials and analyses of new therapeutic agents

SGLT-2 inhibitors

In a post hoc analysis of the DAPA-CKD (Dapagliflozin and Prevention of Adverse Outcomes in Chronic Kidney Disease) trial21 that assessed the effects of an SGLT-2 inhibitor on kidney and cardiovascular outcomes in patients with type 2 diabetes and chronic kidney disease, dapagliflozin reduced the risk of worsening kidney disease, worsening cardiovascular disease, and all-cause mortality across all KDIGO risk categories. These effects were consistent despite the initial KDIGO risk classification, indicating dapagliflozin’s broad applicability to manage chronic kidney disease in patients with diabetes.

A post hoc analysis of the EMPA-REG OUTCOME (Empagliflozin Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients)22 looked at the effects of empagliflozin on favorably changing KDIGO risk categories of patients with type 2 diabetes and chronic kidney disease. Empagliflozin significantly decreased the odds of worsening (odds ratio [OR] 0.70) and increased the odds of improving (OR 1.56) the KDIGO risk category vs placebo. These outcomes highlight empagliflozin’s protective effects across the spectrum of chronic kidney disease risk, which are especially critical for patients with type 2 diabetes, and emphasize its utility to slow chronic kidney disease progression and potentially reverse damage.

Nonsteroidal mineralocorticoid antagonist

In the FIGARO-DKD (Finerenone in Reducing Cardiovascular Mortality and Morbidity in Diabetic Kidney Disease) trial,23 finerenone reduced cardiovascular events in patients with type 2 diabetes and chronic kidney disease, including a 13% relative reduction in a composite end point of death from cardiovascular causes, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure.

Complementing this, the FIDELIO-DKD (Finerenone in Reducing Kidney Failure and Disease Progression in Diabetic Kidney Disease) trial24 focused on kidney outcomes, reporting an 18% reduction consistently across KDIGO risk categories in the primary composite end point of kidney failure, a sustained eGFR decrease of 40% or more from baseline, or death from renal causes.

The FIDELITY (The Finerenone in Chronic Kidney Disease and Type 2 Diabetes: Combined FIDELIO-DKD and FIGARO-DKD Trial Programme Analysis)7 analysis confirmed finerenone’s consistent benefits across KDIGO risk categories, showing a hazard ratio (HR) of 0.77 for the composite kidney outcome and 0.86 for the composite cardiovascular outcome.

FINEARTS-HF (Finerenone Trial to Investigate Efficacy and Safety Superior to Placebo in Patients With Heart Failure)25 extended these insights to patients with heart failure with mildly reduced or preserved ejection fraction, showing a 16% reduction in the composite risk of hospitalization for heart failure or cardiovascular death.

FINE-HEART,26 a pooled analysis of the FIDELIO-DKD, FIGARO-DKD, and FINEARTS-HF trials, reinforced finerenone’s anti-inflammatory and antifibrotic benefits, highlighting its capacity to slow disease progression in both the heart and kidneys.

Finally, the recently completed FINE-ONE (Finerenone Efficacy and Safety in Chronic Kidney Disease and Type One Diabetes) trial19 showed that finerenone significantly reduced albumin-to-creatinine ratio in patients with chronic kidney disease and type 1 diabetes, a group of patients who have been excluded from previous clinical trials evaluating new therapies for diabetes. Data from this trial showed a 25% risk reduction in urinary albumin-to-creatinine ratio, which was used as a bridging biomarker, over 6 months with finerenone vs placebo (least-squares geometric mean ratio 0.75, 95% confidence interval [CI] 0.65–0.87, P = .001), suggesting that it could possibly be a new treatment for these patients in the future.

Together, these studies establish finerenone as a versatile therapy capable of improving outcomes across KDIGO risk categories by targeting shared mechanisms of mineralocorticoid receptor overactivation.30

GLP-1 receptor agonists

A 2024 post hoc analysis of SUSTAIN-6 (Trial to Evaluate Cardiovascular and Other Long-Term Outcomes With Semaglutide in Subjects With Type 2 Diabetes)27 assessed semaglutide vs placebo for kidney disease outcomes across KDIGO risk categories in patients with type 2 diabetes and high cardiovascular disease risk. A 36% reduction was found in the risk of a composite kidney disease end point of persistent macroalbuminuria, doubling of serum creatinine with eGFR less than 45 mL/min/1.73 m2, kidney failure requiring dialysis or transplant, or death due to kidney disease, and this reduced risk was consistent across all KDIGO categories. Semaglutide similarly reduced eGFR decline and urinary albumin-to-creatinine ratio across KDIGO risk categories. Patients treated with semaglutide were more likely to transition to a lower KDIGO risk category (OR 1.69; 95% CI 1.32–2.16, P < .0001) and less likely to progress to a higher risk category (OR 0.71, 95% CI 0.59–0.86, P < .0003). These findings were mainly driven by reductions in albuminuria and improved eGFR.

The FLOW (Evaluate Renal Function With Semaglutide Once Weekly) trial,20 published after the 2024 KDIGO update, was the first to provide primary kidney disease outcome data for a GLP-1 receptor agonist (subcutaneous semaglutide) in patients with type 2 diabetes and chronic kidney disease. The primary composite kidney outcome of a 50% or greater eGFR decline, kidney failure (eGFR < 15 mL/min/1.73 m2, dialysis, or transplantation), or death due to kidney disease or cardiovascular disease was reduced by 24% (hazard ratio 0.76, 95% CI 0.66–0.88, P = .0003). These findings were consistent across a range of subgroups that included demographics, eGFR, urinary albumin-to-creatinine ratio, metabolic parameters, and cardiovascular disease status. Importantly, the rate of eGFR decline significantly slowed, and the risks of major adverse cardiovascular events and all-cause mortality were reduced.

The SELECT (Semaglutide Effects on Cardiovascular Outcomes in People With Overweight or Obesity) trial,28 a cardiovascular outcomes study of semaglutide 2.4 mg weekly vs placebo in patients with overweight or obesity and preexisting cardiovascular disease and no diabetes, showed a 20% relative risk reduction in the primary end point of major adverse cardiac events, which included nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. Additionally, the kidney composite end point of persistent macroalbuminuria onset, persistent 50% or greater eGFR decline, kidney failure, or death due to kidney disease was reduced (hazard ratio 0.78, 95% CI 0.63–0.96, P = .02).29 These findings suggest that semaglutide could help prevent chronic kidney disease in individuals with overweight or obesity without diabetes.

Combination therapy

The CONFIDENCE (Combination Effect of Finerenone and Empagliflozin in Participants With Chronic Kidney Disease and Type 2 Diabetes Using a Urinary Albumin-to-Creatinine Ratio Endpoint) trial31 found that combining finerenone and empagliflozin in patients with type 2 diabetes and chronic kidney disease who were already taking renin-angiotensin system inhibitors reduced the urinary albumin-to-creatinine ratio by 52%, which was greater than reductions seen with finerenone alone (29%) and empagliflozin alone (32%), supporting combination therapy.

Future guideline updates

These studies highlight the importance of using KDIGO risk categories before starting guideline-directed medical therapy to improve outcomes for patients across different levels of risk. The insights gained will help inform treatment plans aimed at reducing progression or inducing regression of KDIGO risk category for patients with cardiovascular-kidney-metabolic syndrome. Future updates are expected to KDIGO and other guidelines on GLP-1 receptor agonists for patients with chronic kidney disease, with or without diabetes. Also, efforts are currently underway to further harmonize guidelines across the specialties of cardiology, endocrinology, and nephrology, aiming to further improve outcomes for these patients.

OVERCOMING POTENTIAL IMPLEMENTATION CHALLENGES

Personalizing risk prediction

Although 2 patients may fall within the same risk category on the KDIGO heat map, their absolute risk of adverse outcomes can differ greatly due to factors such as the underlying cause of chronic kidney disease, demographics, comorbidities, lifestyle, and socioeconomic status. This variation makes personalized risk prediction essential for accurate treatment.

Risk equations have been externally validated across diverse populations and demonstrate solid predictive accuracy:

  • Kidney Failure Risk Equation32,33

  • Kaiser Permanente Northwest equation33

  • Landray model34

  • Z6 score.35

These tools incorporate variables such as age, sex, eGFR, albuminuria, blood pressure, and comorbid conditions to provide more individualized risk estimates, especially as noted earlier for patients with chronic kidney disease stages G3 to G5, which allow clinicians to refine treatment strategies based on a patient’s actual risk rather than relying on general assumptions. In addition to improving personalized care, these tools help optimize nephrology resources, prioritize early use of disease-modifying therapies, and facilitate more meaningful discussions about overall care goals.

Integrating risk stratification into busy clinical practices

While risk prediction tools and the KDIGO heat map offer a straightforward approach to stratifying patients and tailoring interventions, translating these tools into daily clinical practice can be challenging. The risk stratification process is complex, and regularly checking a patient’s eGFR and urinary albumin-to-creatinine ratio can be time consuming, which can lead to underuse in primary care settings. Integrating the heat map with existing electronic health records, which may not always be streamlined or user-friendly, is another significant barrier that could complicate its regular use.36

Targeted education and training programs for clinicians are required to ease integration. These programs should highlight the practical benefits of the KDIGO heat map, demonstrating its role in improving patient outcomes and providing step-by-step guidance on its application in routine practice. Enhancing electronic health record systems to automatically update and visualize KDIGO risk categories within patient profiles could streamline the process, making it easier for clinicians to apply the guidelines consistently.

Another practical solution to improve use is displaying the KDIGO heat map in patient care areas, which ensures easy access for clinicians and patients. This visual aid can facilitate discussions about risk and treatment options, engage patients in their care, and enhance shared decision-making, ultimately improving chronic kidney disease management.

When to consider referral

Clear guidelines for a timely nephrology referral should also be established; we recommend considering referral when a patient is at chronic kidney disease stage 3B or has severely increased albuminuria (A3), in addition to standard clinical triggers such as accelerated hypertension, disproportionate proteinuria, systemic disease findings, or rapidly worsening laboratory values. These referral benchmarks can help ensure that high-risk patients receive specialized care when needed.

Unequal access

An additional challenge to implementation is unequal access to newer medications due to either a lack of health insurance or insufficient insurance coverage, combined with the high cost of these drugs. It is imperative that healthcare policies address this barrier, given the significant kidney and cardiovascular health benefits of these medications.

FUTURE RESEARCH

The evolution of the KDIGO heat map presents several research opportunities. Investigating its real-world effectiveness in various chronic kidney disease care settings could provide insights into its utility and areas for improvement. Studies could also explore how the heat map influences clinical decision-making and patient outcomes compared with conventional care methods. Moreover, integrating the heat map with artificial intelligence could pave the way for predictive analytics that proactively suggest interventions based on a patient’s projected risk trajectory, potentially transforming chronic kidney disease management.

CONCLUSION

The KDIGO heat map is a useful visual tool for the primary care setting that stratifies patients into distinct risk categories based on their eGFR and urinary albumin-to-creatinine ratio. Integrating the KDIGO heat map into clinical practice can have some important benefits:

  • Enable members of the healthcare team to tailor interventions that proactively manage chronic kidney disease progression

  • Enhance diagnostic and prognostic precision and provide a structured approach to patient education, which helps to demystify the complexities of cardiovascular-kidney-metabolic health for patients

  • Reduce risks of major kidney and cardiovascular events by facilitating early chronic kidney disease detection and intervention

  • Support timely and appropriate care by enabling members of the healthcare team to make informed, data-driven decisions, potentially decreasing the burden on healthcare systems and improving patient quality of life

  • Enhance patient comprehension and engagement in their health management, fostering a more collaborative care environment.

DISCLOSURES

Dr. Galindo has disclosed consulting for Amgen, Bayer, Boehringer Ingelheim, Dexcom, Eli Lilly, Gan & Lee Pharmaceutical Co Ltd, and Novo Nordisk, and serving as a research principal investigator for Boehringer Ingelheim, Genentech/Roche Research, and Novo Nordisk. Dr. Shubrook has disclosed serving as a local principal investigator for Abbott Diabetes Care and Breakthrough T1D; consulting for Abbott Diagnostic, Boehringer Ingelheim, and Sanofi; teaching and speaking for Bayer Healthcare; being an advisor or review panel participant for Idorsia Pharmaceuticals Ltd and Novo Nordisk, Inc; and other activities from which remuneration is received or expected from Novo Nordisk, Inc. Dr. Tuttle has disclosed serving as an advisor or review panel participant for Alnylam Pharmaceuticals, GSK, Novo Nordisk, Otsuka Pharmaceutical, Roche, and Travere Therapeutics; consulting for Astra Zeneca, Boehringer Ingelheim, Eli Lilly, and Novo Nordisk; serving as a research co-principal investigator for Bayer; and serving as a research principal investigator for Eli Lilly, Novo Nordisk, and Otsuka Pharmaceutical. The other authors report no relevant financial relationships which, in the context of their contributions, could be perceived as a potential conflict of interest.

ACKNOWLEDGMENTS

The authors thank Darren Lynn, MD, and Sarah Bubeck, PhD, of JPA Health, for medical writing assistance and editorial support. Support for this assistance was provided by Bayer.

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